In [1]:
#!/usr/bin/python
#coding=utf-8
import numpy as np
import pandas as pd
from openpyxl import load_workbook
from xlrd import open_workbook
import csv
from csv import reader
import re
import os
from matplotlib import pyplot
from matplotlib import style
style.use("ggplot")
#%matplotlib notebook
import sys
import operator
import datetime
import time
import matplotlib.pyplot as plt
#%matplotlib notebook
In [2]:
def diffdates(d1, d2):
#Date format: %Y-%m-%d %H:%M:%S
return (time.mktime(time.strptime(d2,'%Y-%m-%d %H:%M:%S')) - time.mktime(time.strptime(d1,'%Y-%m-%d %H:%M:%S')))
In [3]:
filename1 = 'bigdata.xlsx'
df = pd.read_excel(filename1, sheetname='Data')
df = df[df.IdentNo.str[-2:] == "_G"]
In [4]:
# Add two more columns
print ("start")
df = df.values
a,b = df.shape
z = np.zeros((a,2))
df = np.hstack((df,z))
a,b = df.shape
print ("end")
start end
In [5]:
#listing all unique products
print ("start")
varia = 0
list1 = []
while (varia < a):
vari = str(df[varia][2]) + "_" + str(df[varia][3])
t = len(list1)
flag = 0
for i in range(t):
if list1[i] == vari:
flag = 1
break
if (flag == 0):
list1.append(vari)
varia += 1
unique_sets = len(list1)
print (list1)
print (unique_sets)
print ("end")
start ['116_1038408484', '115_1038307052', '116_1038307052', '115_1038305585', '116_1038305585', '115_1038408318', '116_1038408318', '115_1038408484', '115_1038305594', '115_1038407804', '116_1038305594', '116_1038407804', '115_1038407806', '116_1038407806', '115_1038408272', '115_1038408104', '116_1038408272', '116_1038408104', '115_1038407334', '115_F005V16903', '116_1038407334', '115_1038408462', '116_1038408462', '115_1038407754', '116_1038407754', '115_1038305588', '116_1038305588', '115_1038407332', '116_1038407332', '115_1038408840', '116_1038408840', '115_1038305359', '116_1038305359'] 33 end
In [6]:
#assigning product number depending on TTNR to the data-set
print ("start")
for i in range(a):
concat= str(df[i][2]) + "_" + str(df[i][3])
for ii in range(unique_sets):
if (list1[ii] == concat):
df[i][4] = ii + 1
break
print ("end")
start end
In [7]:
print ("start")
timers = 180
threshold_analysis = []
individual_variations = []
threshold_analysis.append([])
flag = 0
a,b = df.shape
count = 0
value_prev = "0"
date_prev = "0"
final_array = []
for i in range(a):
if flag == 0:
final_array.append([])
final_array[count].append(df[i][4])
final_array[count].append(df[i][1])
flag = 1
elif df[i][4] != value_prev:
final_array[count].append(date_prev)
count += 1
final_array.append([])
final_array[count].append(df[i][4])
final_array[count].append(df[i][1])
else:
t1 = pd.to_datetime(date_prev)
t2 = pd.to_datetime(df[i][1])
time = pd.Timedelta(t2 - t1).seconds + pd.Timedelta(t2 - t1).days*24*60*60
if (time > timers):
final_array[count].append(date_prev)
count = count + 1
final_array.append([])
final_array[count].append(df[i][4])
final_array[count].append(df[i][1])
df[i][5] = count + 1
value_prev = df[i][4]
date_prev = df[i][1]
if (i == a-1):
final_array[count].append(date_prev)
print (final_array)
np.savetxt("product_type.csv", df, fmt="%s", delimiter=",")
np.savetxt("timelist.csv", final_array, fmt="%s", delimiter=",")
start
[[1, Timestamp('2018-04-02 06:34:01'), Timestamp('2018-04-02 06:44:06')], [1, Timestamp('2018-04-02 06:47:38'), Timestamp('2018-04-02 06:47:38')], [1, Timestamp('2018-04-02 06:50:51'), Timestamp('2018-04-02 06:52:45')], [1, Timestamp('2018-04-02 06:56:41'), Timestamp('2018-04-02 06:57:33')], [1, Timestamp('2018-04-02 07:05:56'), Timestamp('2018-04-02 07:07:26')], [1, Timestamp('2018-04-02 07:17:38'), Timestamp('2018-04-02 07:19:06')], [1, Timestamp('2018-04-02 07:23:03'), Timestamp('2018-04-02 07:23:42')], [1, Timestamp('2018-04-02 07:30:15'), Timestamp('2018-04-02 07:51:57')], [1, Timestamp('2018-04-02 08:09:20'), Timestamp('2018-04-02 08:40:07')], [1, Timestamp('2018-04-02 08:47:04'), Timestamp('2018-04-02 08:48:39')], [1, Timestamp('2018-04-02 09:01:21'), Timestamp('2018-04-02 09:02:08')], [2, Timestamp('2018-04-02 09:27:57'), Timestamp('2018-04-02 09:36:45')], [2, Timestamp('2018-04-02 09:39:47'), Timestamp('2018-04-02 09:40:34')], [2, Timestamp('2018-04-02 09:46:17'), Timestamp('2018-04-02 10:29:57')], [2, Timestamp('2018-04-02 10:33:02'), Timestamp('2018-04-02 10:43:20')], [2, Timestamp('2018-04-02 10:47:34'), Timestamp('2018-04-02 10:49:01')], [2, Timestamp('2018-04-02 10:55:52'), Timestamp('2018-04-02 11:03:21')], [2, Timestamp('2018-04-02 11:07:50'), Timestamp('2018-04-02 11:42:06')], [2, Timestamp('2018-04-02 11:47:45'), Timestamp('2018-04-02 11:56:25')], [3, Timestamp('2018-04-02 12:06:47'), Timestamp('2018-04-02 12:36:26')], [3, Timestamp('2018-04-02 12:42:51'), Timestamp('2018-04-02 12:53:31')], [3, Timestamp('2018-04-02 12:59:11'), Timestamp('2018-04-02 13:02:36')], [3, Timestamp('2018-04-02 13:06:49'), Timestamp('2018-04-02 13:25:01')], [3, Timestamp('2018-04-02 13:29:11'), Timestamp('2018-04-02 13:34:46')], [3, Timestamp('2018-04-02 13:43:58'), Timestamp('2018-04-02 13:57:01')], [3, Timestamp('2018-04-02 14:02:11'), Timestamp('2018-04-02 14:10:28')], [3, Timestamp('2018-04-02 14:15:38'), Timestamp('2018-04-02 14:28:39')], [3, Timestamp('2018-04-02 14:33:45'), Timestamp('2018-04-02 14:37:05')], [4, Timestamp('2018-04-02 15:33:19'), Timestamp('2018-04-02 15:34:33')], [4, Timestamp('2018-04-02 15:38:25'), Timestamp('2018-04-02 15:51:03')], [4, Timestamp('2018-04-02 16:02:07'), Timestamp('2018-04-02 16:02:07')], [4, Timestamp('2018-04-02 16:24:42'), Timestamp('2018-04-02 16:29:52')], [4, Timestamp('2018-04-02 16:33:24'), Timestamp('2018-04-02 16:58:35')], [4, Timestamp('2018-04-02 17:01:39'), Timestamp('2018-04-02 17:08:54')], [4, Timestamp('2018-04-02 17:22:28'), Timestamp('2018-04-02 17:26:09')], [4, Timestamp('2018-04-02 17:29:38'), Timestamp('2018-04-02 17:32:44')], [4, Timestamp('2018-04-02 17:36:15'), Timestamp('2018-04-02 17:38:06')], [4, Timestamp('2018-04-02 17:41:50'), Timestamp('2018-04-02 17:43:17')], [4, Timestamp('2018-04-02 17:46:26'), Timestamp('2018-04-02 18:14:17')], [4, Timestamp('2018-04-02 18:17:25'), Timestamp('2018-04-02 18:23:10')], [4, Timestamp('2018-04-02 18:28:48'), Timestamp('2018-04-02 18:34:09')], [5, Timestamp('2018-04-02 18:42:28'), Timestamp('2018-04-02 18:52:59')], [5, Timestamp('2018-04-02 18:59:39'), Timestamp('2018-04-02 19:23:45')], [5, Timestamp('2018-04-02 19:32:44'), Timestamp('2018-04-02 19:52:01')], [5, Timestamp('2018-04-02 20:00:33'), Timestamp('2018-04-02 20:13:19')], [5, Timestamp('2018-04-02 20:24:52'), Timestamp('2018-04-02 20:29:46')], [5, Timestamp('2018-04-02 20:34:42'), Timestamp('2018-04-02 20:39:44')], [6, Timestamp('2018-04-02 21:02:38'), Timestamp('2018-04-02 21:03:17')], [6, Timestamp('2018-04-02 21:10:23'), Timestamp('2018-04-02 21:18:48')], [6, Timestamp('2018-04-02 21:23:05'), Timestamp('2018-04-02 21:27:15')], [6, Timestamp('2018-04-02 21:33:03'), Timestamp('2018-04-02 21:37:35')], [6, Timestamp('2018-04-02 21:40:46'), Timestamp('2018-04-02 21:54:06')], [6, Timestamp('2018-04-02 22:00:37'), Timestamp('2018-04-02 22:17:32')], [6, Timestamp('2018-04-02 22:21:27'), Timestamp('2018-04-02 22:23:28')], [6, Timestamp('2018-04-02 22:27:08'), Timestamp('2018-04-02 22:34:48')], [6, Timestamp('2018-04-02 22:38:46'), Timestamp('2018-04-02 23:53:19')], [6, Timestamp('2018-04-02 23:56:35'), Timestamp('2018-04-03 00:35:37')], [6, Timestamp('2018-04-03 00:40:14'), Timestamp('2018-04-03 00:43:03')], [6, Timestamp('2018-04-03 00:46:42'), Timestamp('2018-04-03 01:02:30')], [7, Timestamp('2018-04-03 01:40:45'), Timestamp('2018-04-03 01:53:24')], [7, Timestamp('2018-04-03 01:57:26'), Timestamp('2018-04-03 02:42:43')], [7, Timestamp('2018-04-03 02:47:37'), Timestamp('2018-04-03 03:26:57')], [7, Timestamp('2018-04-03 03:32:07'), Timestamp('2018-04-03 03:55:31')], [7, Timestamp('2018-04-03 04:08:56'), Timestamp('2018-04-03 05:04:37')], [7, Timestamp('2018-04-03 05:12:16'), Timestamp('2018-04-03 05:20:06')], [7, Timestamp('2018-04-03 05:24:07'), Timestamp('2018-04-03 05:39:55')], [8, Timestamp('2018-04-03 06:02:56'), Timestamp('2018-04-03 06:04:19')], [8, Timestamp('2018-04-03 10:59:54'), Timestamp('2018-04-03 11:04:54')], [8, Timestamp('2018-04-03 11:09:01'), Timestamp('2018-04-03 11:30:18')], [8, Timestamp('2018-04-03 11:35:10'), Timestamp('2018-04-03 11:47:22')], [8, Timestamp('2018-04-03 11:53:49'), Timestamp('2018-04-03 11:54:56')], [8, Timestamp('2018-04-03 12:09:38'), Timestamp('2018-04-03 12:09:38')], [8, Timestamp('2018-04-03 12:13:25'), Timestamp('2018-04-03 12:21:15')], [8, Timestamp('2018-04-03 12:28:45'), Timestamp('2018-04-03 12:32:17')], [8, Timestamp('2018-04-03 12:35:26'), Timestamp('2018-04-03 12:42:12')], [8, Timestamp('2018-04-03 12:48:45'), Timestamp('2018-04-03 12:48:45')], [8, Timestamp('2018-04-03 12:55:27'), Timestamp('2018-04-03 12:58:04')], [8, Timestamp('2018-04-03 13:32:46'), Timestamp('2018-04-03 13:32:46')], [8, Timestamp('2018-04-03 13:42:33'), Timestamp('2018-04-03 13:43:24')], [8, Timestamp('2018-04-03 13:50:09'), Timestamp('2018-04-03 13:56:56')], [8, Timestamp('2018-04-03 14:17:02'), Timestamp('2018-04-03 14:18:41')], [8, Timestamp('2018-04-03 14:24:17'), Timestamp('2018-04-03 14:35:41')], [8, Timestamp('2018-04-03 14:38:59'), Timestamp('2018-04-03 15:04:49')], [8, Timestamp('2018-04-03 15:09:54'), Timestamp('2018-04-03 15:21:13')], [1, Timestamp('2018-04-03 15:29:04'), Timestamp('2018-04-03 15:39:19')], [1, Timestamp('2018-04-03 15:43:47'), Timestamp('2018-04-03 15:49:16')], [1, Timestamp('2018-04-03 15:56:50'), Timestamp('2018-04-03 15:58:36')], [1, Timestamp('2018-04-03 16:09:51'), Timestamp('2018-04-03 16:19:23')], [1, Timestamp('2018-04-03 16:25:32'), Timestamp('2018-04-03 16:33:28')], [1, Timestamp('2018-04-03 16:44:17'), Timestamp('2018-04-03 16:53:16')], [1, Timestamp('2018-04-03 16:57:36'), Timestamp('2018-04-03 16:57:36')], [1, Timestamp('2018-04-03 17:02:53'), Timestamp('2018-04-03 17:16:35')], [1, Timestamp('2018-04-03 17:22:14'), Timestamp('2018-04-03 17:28:55')], [1, Timestamp('2018-04-03 17:35:13'), Timestamp('2018-04-03 17:43:40')], [1, Timestamp('2018-04-03 17:46:55'), Timestamp('2018-04-03 18:02:00')], [4, Timestamp('2018-04-03 18:35:52'), Timestamp('2018-04-03 18:37:33')], [4, Timestamp('2018-04-03 18:47:37'), Timestamp('2018-04-03 19:20:55')], [4, Timestamp('2018-04-03 19:25:05'), Timestamp('2018-04-03 19:29:07')], [4, Timestamp('2018-04-03 19:35:21'), Timestamp('2018-04-03 20:06:23')], [4, Timestamp('2018-04-03 20:10:50'), Timestamp('2018-04-03 20:31:06')], [5, Timestamp('2018-04-03 20:41:23'), Timestamp('2018-04-03 21:13:06')], [5, Timestamp('2018-04-03 21:16:46'), Timestamp('2018-04-03 21:28:35')], [5, Timestamp('2018-04-03 21:32:01'), Timestamp('2018-04-03 21:38:58')], [5, Timestamp('2018-04-03 21:43:13'), Timestamp('2018-04-03 21:55:19')], [5, Timestamp('2018-04-03 21:59:10'), Timestamp('2018-04-03 22:06:39')], [5, Timestamp('2018-04-03 22:10:41'), Timestamp('2018-04-03 22:26:51')], [9, Timestamp('2018-04-03 22:36:52'), Timestamp('2018-04-03 23:15:54')], [9, Timestamp('2018-04-03 23:22:44'), Timestamp('2018-04-03 23:45:41')], [9, Timestamp('2018-04-03 23:49:01'), Timestamp('2018-04-04 00:06:12')], [9, Timestamp('2018-04-04 00:09:30'), Timestamp('2018-04-04 00:11:30')], [10, Timestamp('2018-04-04 00:36:31'), Timestamp('2018-04-04 00:39:47')], [10, Timestamp('2018-04-04 00:44:06'), Timestamp('2018-04-04 00:56:06')], [10, Timestamp('2018-04-04 01:00:58'), Timestamp('2018-04-04 01:05:43')], [10, Timestamp('2018-04-04 01:35:55'), Timestamp('2018-04-04 02:26:01')], [11, Timestamp('2018-04-04 02:35:36'), Timestamp('2018-04-04 03:07:11')], [11, Timestamp('2018-04-04 03:12:44'), Timestamp('2018-04-04 03:19:30')], [11, Timestamp('2018-04-04 03:23:01'), Timestamp('2018-04-04 03:39:29')], [11, Timestamp('2018-04-04 03:44:17'), Timestamp('2018-04-04 04:09:05')], [11, Timestamp('2018-04-04 04:14:23'), Timestamp('2018-04-04 04:16:24')], [12, Timestamp('2018-04-04 04:38:27'), Timestamp('2018-04-04 04:46:02')], [12, Timestamp('2018-04-04 04:52:50'), Timestamp('2018-04-04 04:56:58')], [12, Timestamp('2018-04-04 05:00:33'), Timestamp('2018-04-04 05:08:06')], [12, Timestamp('2018-04-04 05:14:51'), Timestamp('2018-04-04 05:28:16')], [12, Timestamp('2018-04-04 05:31:17'), Timestamp('2018-04-04 05:36:53')], [12, Timestamp('2018-04-04 05:43:26'), Timestamp('2018-04-04 05:46:17')], [12, Timestamp('2018-04-04 06:01:20'), Timestamp('2018-04-04 06:02:24')], [12, Timestamp('2018-04-04 06:58:48'), Timestamp('2018-04-04 07:02:26')], [12, Timestamp('2018-04-04 07:12:31'), Timestamp('2018-04-04 07:16:38')], [12, Timestamp('2018-04-04 07:23:22'), Timestamp('2018-04-04 07:33:39')], [12, Timestamp('2018-04-04 08:29:09'), Timestamp('2018-04-04 08:29:09')], [12, Timestamp('2018-04-04 08:55:36'), Timestamp('2018-04-04 09:13:08')], [6, Timestamp('2018-04-04 09:38:29'), Timestamp('2018-04-04 09:38:30')], [6, Timestamp('2018-04-04 09:47:57'), Timestamp('2018-04-04 11:21:17')], [7, Timestamp('2018-04-04 12:06:36'), Timestamp('2018-04-04 12:17:14')], [7, Timestamp('2018-04-04 12:25:30'), Timestamp('2018-04-04 12:29:34')], [7, Timestamp('2018-04-04 12:33:04'), Timestamp('2018-04-04 12:34:58')], [7, Timestamp('2018-04-04 13:23:34'), Timestamp('2018-04-04 13:26:24')], [7, Timestamp('2018-04-04 13:29:48'), Timestamp('2018-04-04 13:29:48')], [7, Timestamp('2018-04-04 13:35:19'), Timestamp('2018-04-04 14:24:22')], [7, Timestamp('2018-04-04 14:28:58'), Timestamp('2018-04-04 14:47:10')], [4, Timestamp('2018-04-04 14:59:41'), Timestamp('2018-04-04 15:16:31')], [4, Timestamp('2018-04-04 15:41:34'), Timestamp('2018-04-04 16:00:31')], [4, Timestamp('2018-04-04 16:04:15'), Timestamp('2018-04-04 16:12:38')], [4, Timestamp('2018-04-04 16:15:40'), Timestamp('2018-04-04 16:15:40')], [4, Timestamp('2018-04-04 16:20:10'), Timestamp('2018-04-04 16:24:45')], [4, Timestamp('2018-04-04 16:29:03'), Timestamp('2018-04-04 16:32:02')], [4, Timestamp('2018-04-04 16:35:11'), Timestamp('2018-04-04 16:53:30')], [4, Timestamp('2018-04-04 16:57:02'), Timestamp('2018-04-04 17:02:20')], [4, Timestamp('2018-04-04 17:08:37'), Timestamp('2018-04-04 17:08:37')], [4, Timestamp('2018-04-04 17:12:52'), Timestamp('2018-04-04 17:14:44')], [4, Timestamp('2018-04-04 17:22:03'), Timestamp('2018-04-04 17:22:03')], [4, Timestamp('2018-04-04 17:36:45'), Timestamp('2018-04-04 17:47:23')], [5, Timestamp('2018-04-04 17:59:33'), Timestamp('2018-04-04 17:59:33')], [5, Timestamp('2018-04-04 18:07:17'), Timestamp('2018-04-04 18:22:01')], [5, Timestamp('2018-04-04 18:27:59'), Timestamp('2018-04-04 19:05:39')], [5, Timestamp('2018-04-04 19:13:12'), Timestamp('2018-04-04 19:19:12')], [5, Timestamp('2018-04-04 19:30:39'), Timestamp('2018-04-04 19:38:10')], [5, Timestamp('2018-04-04 19:42:15'), Timestamp('2018-04-04 19:50:58')], [9, Timestamp('2018-04-04 19:57:17'), Timestamp('2018-04-04 20:09:36')], [9, Timestamp('2018-04-04 20:13:07'), Timestamp('2018-04-04 20:18:54')], [9, Timestamp('2018-04-04 20:21:56'), Timestamp('2018-04-04 20:25:20')], [9, Timestamp('2018-04-04 20:34:24'), Timestamp('2018-04-04 20:49:54')], [9, Timestamp('2018-04-04 20:55:20'), Timestamp('2018-04-04 21:14:57')], [11, Timestamp('2018-04-04 21:23:50'), Timestamp('2018-04-04 21:23:50')], [11, Timestamp('2018-04-04 21:28:17'), Timestamp('2018-04-04 21:42:16')], [11, Timestamp('2018-04-04 21:46:19'), Timestamp('2018-04-04 21:54:45')], [11, Timestamp('2018-04-04 21:58:05'), Timestamp('2018-04-04 22:11:20')], [11, Timestamp('2018-04-04 22:15:50'), Timestamp('2018-04-04 22:23:23')], [8, Timestamp('2018-04-04 23:04:35'), Timestamp('2018-04-04 23:15:53')], [8, Timestamp('2018-04-04 23:21:14'), Timestamp('2018-04-04 23:21:59')], [8, Timestamp('2018-04-04 23:25:02'), Timestamp('2018-04-04 23:33:23')], [8, Timestamp('2018-04-04 23:38:08'), Timestamp('2018-04-04 23:45:14')], [8, Timestamp('2018-04-04 23:52:42'), Timestamp('2018-04-04 23:58:29')], [8, Timestamp('2018-04-05 00:02:00'), Timestamp('2018-04-05 00:48:48')], [8, Timestamp('2018-04-05 00:52:15'), Timestamp('2018-04-05 01:03:21')], [1, Timestamp('2018-04-05 01:08:55'), 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Timestamp('2018-04-28 11:34:32')], [1, Timestamp('2018-04-28 11:42:42'), Timestamp('2018-04-28 11:42:42')], [1, Timestamp('2018-04-28 11:49:52'), Timestamp('2018-04-28 11:52:16')], [2, Timestamp('2018-04-28 12:24:54'), Timestamp('2018-04-28 12:48:13')], [2, Timestamp('2018-04-28 12:51:51'), Timestamp('2018-04-28 12:52:24')], [2, Timestamp('2018-04-28 12:56:02'), Timestamp('2018-04-28 13:39:37')], [2, Timestamp('2018-04-28 13:44:04'), Timestamp('2018-04-28 13:54:24')], [3, Timestamp('2018-04-28 14:01:44'), Timestamp('2018-04-28 14:10:43')], [3, Timestamp('2018-04-28 14:13:45'), Timestamp('2018-04-28 14:41:06')], [3, Timestamp('2018-04-28 14:45:53'), Timestamp('2018-04-28 14:59:51')], [3, Timestamp('2018-04-28 15:03:01'), Timestamp('2018-04-28 15:13:04')], [3, Timestamp('2018-04-28 15:17:45'), Timestamp('2018-04-28 15:25:19')], [3, Timestamp('2018-04-28 15:28:33'), Timestamp('2018-04-28 16:03:06')], [3, Timestamp('2018-04-28 16:09:00'), Timestamp('2018-04-28 16:30:04')], [9, Timestamp('2018-04-28 16:36:25'), Timestamp('2018-04-28 17:04:45')], [9, Timestamp('2018-04-28 17:07:57'), Timestamp('2018-04-28 17:36:43')], [9, Timestamp('2018-04-28 17:41:40'), Timestamp('2018-04-28 18:12:54')], [13, Timestamp('2018-04-28 18:20:45'), Timestamp('2018-04-28 18:20:45')], [13, Timestamp('2018-04-28 18:26:00'), Timestamp('2018-04-28 19:14:39')], [13, Timestamp('2018-04-28 19:20:40'), Timestamp('2018-04-28 19:29:38')], [13, Timestamp('2018-04-28 19:34:41'), Timestamp('2018-04-28 19:36:05')], [11, Timestamp('2018-04-28 19:50:57'), Timestamp('2018-04-28 19:51:20')], [11, Timestamp('2018-04-28 19:55:53'), Timestamp('2018-04-28 20:20:31')], [11, Timestamp('2018-04-28 20:24:58'), Timestamp('2018-04-28 20:43:15')], [11, Timestamp('2018-04-28 20:48:06'), Timestamp('2018-04-28 21:23:51')], [11, Timestamp('2018-04-28 21:28:44'), Timestamp('2018-04-28 21:30:34')], [14, Timestamp('2018-04-28 21:34:59'), Timestamp('2018-04-28 21:44:14')], [14, Timestamp('2018-04-28 21:50:00'), Timestamp('2018-04-28 22:04:52')], [14, Timestamp('2018-04-28 22:15:33'), Timestamp('2018-04-28 22:30:02')], [14, Timestamp('2018-04-28 22:33:31'), Timestamp('2018-04-28 22:43:44')], [10, Timestamp('2018-04-28 23:12:56'), Timestamp('2018-04-29 00:28:30')], [10, Timestamp('2018-04-29 00:35:24'), Timestamp('2018-04-29 00:40:46')], [10, Timestamp('2018-04-29 00:50:06'), Timestamp('2018-04-29 00:55:52')], [10, Timestamp('2018-04-29 01:00:55'), Timestamp('2018-04-29 01:37:56')], [12, Timestamp('2018-04-29 01:48:05'), Timestamp('2018-04-29 01:49:17')], [12, Timestamp('2018-04-29 01:54:50'), Timestamp('2018-04-29 01:54:50')], [12, Timestamp('2018-04-29 02:04:37'), Timestamp('2018-04-29 02:08:18')], [12, Timestamp('2018-04-29 02:13:25'), Timestamp('2018-04-29 02:36:10')], [12, Timestamp('2018-04-29 02:42:13'), Timestamp('2018-04-29 02:47:10')], [12, Timestamp('2018-04-29 02:53:20'), Timestamp('2018-04-29 02:57:35')], [12, Timestamp('2018-04-29 03:01:11'), Timestamp('2018-04-29 03:03:00')], [12, Timestamp('2018-04-29 03:10:08'), Timestamp('2018-04-29 03:20:55')], [12, Timestamp('2018-04-29 03:24:05'), Timestamp('2018-04-29 03:39:54')], [12, Timestamp('2018-04-29 03:46:03'), Timestamp('2018-04-29 04:08:15')], [12, Timestamp('2018-04-29 04:11:55'), Timestamp('2018-04-29 04:31:04')], [12, Timestamp('2018-04-29 04:34:07'), Timestamp('2018-04-29 04:36:47')], [12, Timestamp('2018-04-29 04:41:07'), Timestamp('2018-04-29 04:57:55')], [12, Timestamp('2018-04-29 05:05:17'), Timestamp('2018-04-29 05:09:09')], [12, Timestamp('2018-04-29 05:15:30'), Timestamp('2018-04-29 05:17:31')], [12, Timestamp('2018-04-29 05:23:10'), Timestamp('2018-04-29 05:56:32')], [22, Timestamp('2018-04-29 06:24:38'), Timestamp('2018-04-29 06:30:31')], [22, Timestamp('2018-04-29 06:34:40'), Timestamp('2018-04-29 06:38:06')], [22, Timestamp('2018-04-29 06:42:55'), Timestamp('2018-04-29 06:46:53')], [22, Timestamp('2018-04-29 06:56:24'), Timestamp('2018-04-29 07:05:16')], [22, Timestamp('2018-04-29 07:09:32'), Timestamp('2018-04-29 07:11:46')], [22, Timestamp('2018-04-29 07:20:48'), Timestamp('2018-04-29 07:34:15')], [22, Timestamp('2018-04-29 07:37:40'), Timestamp('2018-04-29 07:37:40')], [22, Timestamp('2018-04-29 07:40:45'), Timestamp('2018-04-29 07:41:30')], [22, Timestamp('2018-04-29 07:45:20'), Timestamp('2018-04-29 07:56:19')], [23, Timestamp('2018-04-29 08:06:40'), Timestamp('2018-04-29 08:06:40')], [23, Timestamp('2018-04-29 08:13:09'), Timestamp('2018-04-29 08:15:32')], [23, Timestamp('2018-04-29 08:25:41'), Timestamp('2018-04-29 08:32:05')], [23, Timestamp('2018-04-29 08:48:47'), Timestamp('2018-04-29 08:49:12')], [23, Timestamp('2018-04-29 08:53:30'), Timestamp('2018-04-29 09:07:38')], [23, Timestamp('2018-04-29 09:11:16'), Timestamp('2018-04-29 09:11:16')], [23, Timestamp('2018-04-29 09:19:49'), Timestamp('2018-04-29 09:20:36')], [23, Timestamp('2018-04-29 09:23:50'), Timestamp('2018-04-29 09:27:46')], [23, Timestamp('2018-04-29 09:32:45'), Timestamp('2018-04-29 09:43:41')], [23, Timestamp('2018-04-29 09:48:26'), Timestamp('2018-04-29 09:51:56')], [16, Timestamp('2018-04-29 09:56:49'), Timestamp('2018-04-29 10:11:44')], [16, Timestamp('2018-04-29 10:15:36'), Timestamp('2018-04-29 11:01:01')], [16, Timestamp('2018-04-29 11:05:08'), Timestamp('2018-04-29 11:08:03')], [16, Timestamp('2018-04-29 11:11:25'), Timestamp('2018-04-29 11:26:24')], [16, Timestamp('2018-04-29 11:30:04'), Timestamp('2018-04-29 11:36:08')], [16, Timestamp('2018-04-29 11:41:07'), Timestamp('2018-04-29 11:50:43')], [16, Timestamp('2018-04-29 11:55:41'), Timestamp('2018-04-29 11:57:25')], [18, Timestamp('2018-04-29 12:00:34'), Timestamp('2018-04-29 12:04:00')], [18, Timestamp('2018-04-29 12:11:36'), Timestamp('2018-04-29 12:13:51')], [18, Timestamp('2018-04-29 12:19:46'), Timestamp('2018-04-29 12:20:42')], [18, Timestamp('2018-04-29 12:24:37'), Timestamp('2018-04-29 12:36:22')], [18, Timestamp('2018-04-29 12:45:06'), Timestamp('2018-04-29 12:50:45')], [18, Timestamp('2018-04-29 12:55:22'), Timestamp('2018-04-29 13:09:31')], [18, Timestamp('2018-04-29 13:12:56'), Timestamp('2018-04-29 13:15:19')], [18, Timestamp('2018-04-29 13:18:57'), Timestamp('2018-04-29 13:31:14')], [18, Timestamp('2018-04-29 13:37:23'), Timestamp('2018-04-29 13:45:00')], [18, Timestamp('2018-04-29 13:49:37'), Timestamp('2018-04-29 13:56:52')], [4, Timestamp('2018-04-29 14:16:17'), Timestamp('2018-04-29 14:23:12')], [4, Timestamp('2018-04-29 14:26:13'), Timestamp('2018-04-29 15:28:49')], [4, Timestamp('2018-04-29 15:32:44'), Timestamp('2018-04-29 16:07:25')], [4, Timestamp('2018-04-29 16:12:50'), Timestamp('2018-04-29 16:39:19')], [5, Timestamp('2018-04-29 17:07:10'), Timestamp('2018-04-29 17:26:03')], [5, Timestamp('2018-04-29 17:30:08'), Timestamp('2018-04-29 18:45:52')], [5, Timestamp('2018-04-29 18:48:55'), Timestamp('2018-04-29 19:08:38')], [5, Timestamp('2018-04-29 19:12:58'), Timestamp('2018-04-29 19:36:52')], [10, Timestamp('2018-04-29 19:53:18'), Timestamp('2018-04-29 20:08:11')], [10, Timestamp('2018-04-29 20:13:45'), Timestamp('2018-04-29 21:29:27')], [12, Timestamp('2018-04-29 21:38:58'), Timestamp('2018-04-29 21:46:37')], [12, Timestamp('2018-04-29 21:50:51'), Timestamp('2018-04-29 21:52:56')], [12, Timestamp('2018-04-29 21:56:17'), Timestamp('2018-04-29 22:15:39')], [12, Timestamp('2018-04-29 22:18:57'), Timestamp('2018-04-29 23:46:55')], [12, Timestamp('2018-04-29 23:55:31'), Timestamp('2018-04-30 00:01:49')], [4, Timestamp('2018-04-30 00:10:17'), Timestamp('2018-04-30 00:40:32')], [4, Timestamp('2018-04-30 00:48:00'), Timestamp('2018-04-30 00:55:29')], [4, Timestamp('2018-04-30 01:00:13'), Timestamp('2018-04-30 01:09:45')], [4, Timestamp('2018-04-30 01:13:51'), Timestamp('2018-04-30 01:48:20')], [5, Timestamp('2018-04-30 01:51:48'), Timestamp('2018-04-30 02:56:33')], [5, Timestamp('2018-04-30 03:02:27'), Timestamp('2018-04-30 03:03:19')], [5, Timestamp('2018-04-30 03:08:11'), Timestamp('2018-04-30 03:12:47')], [5, Timestamp('2018-04-30 03:19:29'), Timestamp('2018-04-30 03:21:43')], [5, Timestamp('2018-04-30 03:25:15'), Timestamp('2018-04-30 03:31:57')], [5, Timestamp('2018-04-30 03:37:27'), Timestamp('2018-04-30 03:39:11')], [5, Timestamp('2018-04-30 03:44:20'), Timestamp('2018-04-30 03:44:40')], [20, Timestamp('2018-04-30 04:21:29'), Timestamp('2018-04-30 04:22:14')], [20, Timestamp('2018-04-30 04:25:39'), Timestamp('2018-04-30 05:52:48')], [20, Timestamp('2018-04-30 05:56:18'), Timestamp('2018-04-30 05:59:23')], [20, Timestamp('2018-04-30 06:03:32'), Timestamp('2018-04-30 06:07:41')], [20, Timestamp('2018-04-30 06:11:56'), Timestamp('2018-04-30 06:46:42')], [20, Timestamp('2018-04-30 06:49:58'), Timestamp('2018-04-30 07:24:53')], [20, Timestamp('2018-04-30 07:30:38'), Timestamp('2018-04-30 07:51:19')], [20, Timestamp('2018-04-30 07:54:37'), Timestamp('2018-04-30 07:56:34')], [20, Timestamp('2018-04-30 08:00:41'), Timestamp('2018-04-30 08:47:35')], [20, Timestamp('2018-04-30 08:51:41'), Timestamp('2018-04-30 08:54:45')], [20, Timestamp('2018-04-30 08:58:56'), Timestamp('2018-04-30 09:08:31')], [20, Timestamp('2018-04-30 09:12:17'), Timestamp('2018-04-30 10:13:54')], [20, Timestamp('2018-04-30 10:18:08'), Timestamp('2018-04-30 10:40:23')], [8, Timestamp('2018-04-30 10:53:54'), Timestamp('2018-04-30 12:01:15')], [8, Timestamp('2018-04-30 12:11:57'), Timestamp('2018-04-30 12:17:04')], [8, Timestamp('2018-04-30 12:20:18'), Timestamp('2018-04-30 12:32:32')], [8, Timestamp('2018-04-30 12:35:41'), Timestamp('2018-04-30 13:06:35')], [8, Timestamp('2018-04-30 13:10:02'), Timestamp('2018-04-30 13:18:34')], [8, Timestamp('2018-04-30 13:22:59'), Timestamp('2018-04-30 13:33:20')], [1, Timestamp('2018-04-30 13:46:49'), Timestamp('2018-04-30 13:50:56')], [1, Timestamp('2018-04-30 13:54:58'), Timestamp('2018-04-30 15:37:23')], [30, Timestamp('2018-04-30 16:20:42'), Timestamp('2018-04-30 16:45:23')], [30, Timestamp('2018-04-30 16:49:24'), Timestamp('2018-04-30 17:54:21')], [30, Timestamp('2018-04-30 17:58:32'), Timestamp('2018-04-30 18:13:15')], [30, Timestamp('2018-04-30 18:18:40'), Timestamp('2018-04-30 18:53:57')], [31, Timestamp('2018-04-30 19:06:42'), Timestamp('2018-04-30 19:56:50')], [31, Timestamp('2018-04-30 20:07:35'), Timestamp('2018-04-30 20:28:36')], [31, Timestamp('2018-04-30 20:32:08'), Timestamp('2018-04-30 21:22:59')], [31, Timestamp('2018-04-30 21:27:38'), Timestamp('2018-04-30 21:48:26')], [10, Timestamp('2018-04-30 21:58:37'), Timestamp('2018-04-30 22:45:05')], [10, Timestamp('2018-04-30 22:51:19'), Timestamp('2018-04-30 23:59:04')], [12, Timestamp('2018-05-01 00:26:26'), Timestamp('2018-05-01 00:26:26')], [12, Timestamp('2018-05-01 00:31:35'), Timestamp('2018-05-01 00:40:01')], [12, Timestamp('2018-05-01 00:50:06'), Timestamp('2018-05-01 00:53:24')], [12, Timestamp('2018-05-01 00:59:16'), Timestamp('2018-05-01 01:44:49')], [12, Timestamp('2018-05-01 01:48:12'), Timestamp('2018-05-01 02:04:34')], [12, Timestamp('2018-05-01 02:07:52'), Timestamp('2018-05-01 02:12:07')], [12, Timestamp('2018-05-01 02:23:44'), Timestamp('2018-05-01 02:23:44')], [12, Timestamp('2018-05-01 02:26:56'), Timestamp('2018-05-01 02:28:17')], [12, Timestamp('2018-05-01 02:32:52'), Timestamp('2018-05-01 02:42:04')], [12, Timestamp('2018-05-01 02:45:30'), Timestamp('2018-05-01 02:53:58')], [12, Timestamp('2018-05-01 02:58:11'), Timestamp('2018-05-01 03:05:30')], [12, Timestamp('2018-05-01 03:09:35'), Timestamp('2018-05-01 03:11:09')], [12, Timestamp('2018-05-01 03:24:23'), Timestamp('2018-05-01 03:24:23')], [12, Timestamp('2018-05-01 03:30:13'), Timestamp('2018-05-01 03:34:28')], [12, Timestamp('2018-05-01 03:39:34'), Timestamp('2018-05-01 03:39:34')], [12, Timestamp('2018-05-01 03:47:08'), Timestamp('2018-05-01 03:47:08')], [12, Timestamp('2018-05-01 03:51:31'), Timestamp('2018-05-01 03:59:26')], [12, Timestamp('2018-05-01 04:02:34'), Timestamp('2018-05-01 04:04:18')], [12, Timestamp('2018-05-01 04:08:49'), Timestamp('2018-05-01 04:17:51')], [12, Timestamp('2018-05-01 04:21:55'), Timestamp('2018-05-01 04:30:12')], [20, Timestamp('2018-05-01 05:11:01'), Timestamp('2018-05-01 05:26:25')], [20, Timestamp('2018-05-01 05:31:42'), Timestamp('2018-05-01 05:37:51')], [20, Timestamp('2018-05-01 05:44:15'), Timestamp('2018-05-01 05:59:19')]]
In [8]:
length_list = len(final_array)
print (length_list)
buckets = [0] * (unique_sets+1)
for i in range(length_list):
a = final_array[i][0]
buckets[a] += 1
print (buckets)
1664 [0, 147, 76, 65, 223, 209, 87, 57, 123, 45, 57, 33, 178, 22, 23, 9, 34, 13, 45, 15, 54, 29, 19, 20, 16, 14, 2, 2, 10, 6, 9, 12, 5, 5]
In [ ]:
###Code for generating file, alerady generated so using the generated one
filename2 = 'analyses_data.xlsx'
pro_data = pd.read_excel(filename2, sheetname='RealtimeData')
pro_data['TIME'] = pd.to_datetime(pro_data['TIME'])
pro_data = pro_data.values
a,b = pro_data.shape
z = np.zeros((a,2))
pro_data = np.hstack((pro_data,z))
a,b = pro_data.shape
i = 0
print ("start")
while (i < a):
t3 = pro_data[i][0]
flag = 0
j = 0
while (j < length_list):
t1 = pd.to_datetime(final_array[j][1])
t2 = pd.to_datetime(final_array[j][2])
time1 = pd.Timedelta(t2 - t3).seconds + pd.Timedelta(t2 - t3).days*24*60*60
time2 = pd.Timedelta(t3 - t1).seconds + pd.Timedelta(t3 - t1).days*24*60*60
time3 = pd.Timedelta(t2 - t1).seconds + pd.Timedelta(t2 - t1).days*24*60*60
if (time1 >= 0 and time2 >= 0 and time3 >= 0 and (time1+time2 == time3)):
pro_data[i][b-2] = final_array[j][0]
pro_data[i][b-1] = j
flag = 1
break
j += 1
if (flag == 0):
pro_data = np.delete(pro_data, i, 0)
i = i - 1
a,b = pro_data.shape
i += 1
df = pd.DataFrame(pro_data)
filepath = 'my_excel_file.xlsx'
df.to_excel(filepath, index=False)
In [9]:
#filename3 = 'POWER_TTNR.xlsx'
filename3 = 'my_excel_file1.xlsx'
i = 1
############code addded on 22/6/2018#######
print (list1)
mean_standard = []
quantities_n = []
#########end############
#fig, axs = plt.subplots(unique_sets,1,figsize=(100,300))
#plot_count = 0
while (i <= unique_sets):
df1 = pd.read_excel(filename3, sheetname='Sheet1')
df1 = df1[df1.PRODUCT_TYPE == i]
df1 = df1[["TIME","ACTIVEPOWER","PRODUCT_TYPE", "BATCH_TYPE"]]
x = df1.BATCH_TYPE.unique()
################Code started for plotting graph ###################
'''
y_label = "Real Time Power of Product " + str(i)
plt.subplot(221)
plt.plot(df1['TIME'], df1['ACTIVEPOWER'])
plt.title(str(i))
plt.xlabel('time-stamp')
plt.ylabel(y_label)
plt.show()
'''
####################Code ended for plotting graph###########
#df1 = df1.values
#a,b = df1.shape
jj = []
for kk in range(len(x)):
jj.append([])
df2 = df1[df1.BATCH_TYPE == x[kk]]
df2 = df2.values
aa, bb = df2.shape
ref_time = 0
for ll in range(aa):
jj[kk].append(df2[ll][1])
jj = pd.DataFrame(jj)
median_var = jj.median()
standard_deviation_from_mean_value = jj.std()
max_value = jj.max()
min_value = jj.min()
mean_value = jj.mean()
kkkkkkk = pd.Series.tolist(median_var)
kkkkkkk1 = pd.Series.tolist(standard_deviation_from_mean_value)
kkkkkkk1_numpy = np.asarray(kkkkkkk1)
ppp = kkkkkkk1_numpy.size
countttt_batch_size = 0
for variables in range(ppp):
if np.isnan(kkkkkkk1_numpy[variables]):
countttt_batch_size += 1
mean_std_dev = np.nanmean(kkkkkkk1_numpy)
mean_standard.append(mean_std_dev)
quantities_n.append(ppp - countttt_batch_size)
print("standard deviation is " + str(mean_std_dev))
kkkkkkk2 = pd.Series.tolist(max_value)
kkkkkkk3 = pd.Series.tolist(min_value)
kkkkkkk4 = pd.Series.tolist(mean_value)
kkkkkkk5 = [b_i - a_i for a_i, b_i in zip(kkkkkkk1, kkkkkkk4)]
kkkkkkk6 = [b_i + a_i for a_i, b_i in zip(kkkkkkk1, kkkkkkk4)]
y_a = list(range(0, len(kkkkkkk)))
y_label = "Real Time Power of Product " + str(i) + " kW"
plt.figure(1, figsize=(20,10))
plt.subplot(211)
plt.plot(y_a, kkkkkkk, 'r', label='median/predicted')
#plt.plot(y_a, kkkkkkk1, 'k', label='standard deviation from mean value')
plt.plot(y_a, kkkkkkk5, 'brown', label='negative error')
plt.plot(y_a, kkkkkkk6, 'coral', label='positive error')
plt.title("product type" + str(i))
plt.xlabel('time-stamp in minutes')
plt.ylabel(y_label)
plt.legend()
plt.tight_layout()
plt.subplot(212)
plt.plot(y_a, kkkkkkk4, 'bx', label='mean power')
plt.plot(y_a, kkkkkkk2, 'y', label='maximum power')
plt.plot(y_a, kkkkkkk3, 'g', label='minimum power')
plt.title("product type" + str(i))
plt.xlabel('time-stamp in minutes')
plt.ylabel(y_label)
plt.legend()
plt.tight_layout()
plt.show()
##########second phase of code ending#########
i += 1
#plot_count += 1
#print (type(mean_standard))
print (mean_standard)
print (quantities_n)
##list1
###mean_standard
###quantities_n
print (mean_standard)
print (quantities_n)
print (list1)
list1_new = []
mean_standard_new = []
quantities_n_new = []
product_number = []
for i in range(len(mean_standard)):
if str(mean_standard[i]) != 'nan':
mean_standard_new.append(mean_standard[i])
quantities_n_new.append(quantities_n[i])
list1_new.append(list1[i])
product_number.append(i)
mean_standard = mean_standard_new
quantities_n = quantities_n_new
list1 = list1_new
print (mean_standard)
print (quantities_n)
print (list1)
print (product_number)
width = 0.5
y_pos = np.arange(len(list1))
fig, ax = plt.subplots(figsize=(80, 40))
rects = ax.bar(y_pos, mean_standard, width ,color='r', align='edge' ,alpha=0.5)
ax.set_xticks(y_pos + width/2.)
ax.set_xticklabels(list1, fontsize=10)
ax.set_xlabel('TTNR')
ax.set_ylabel('standard deviation')
plt.legend()
def autolabel(rects,ii):
for rect in rects:
height = rect.get_height()
values = quantities_n[ii]
value1 = product_number[ii]
value2 = mean_standard[ii]
ii += 1
ax.text(rect.get_x() + rect.get_width()/2., 1.08*height,
'%s & prod. %d & std %f' %(str(values) , value1, value2),
ha='center', va='bottom', fontsize=40)
autolabel(rects, 0)
plt.tight_layout()
plt.show()
['116_1038408484', '115_1038307052', '116_1038307052', '115_1038305585', '116_1038305585', '115_1038408318', '116_1038408318', '115_1038408484', '115_1038305594', '115_1038407804', '116_1038305594', '116_1038407804', '115_1038407806', '116_1038407806', '115_1038408272', '115_1038408104', '116_1038408272', '116_1038408104', '115_1038407334', '115_F005V16903', '116_1038407334', '115_1038408462', '116_1038408462', '115_1038407754', '116_1038407754', '115_1038305588', '116_1038305588', '115_1038407332', '116_1038407332', '115_1038408840', '116_1038408840', '115_1038305359', '116_1038305359'] standard deviation is 3.49936124956
standard deviation is 3.68703065716
standard deviation is 3.74880587682
standard deviation is 3.03753382962
standard deviation is 3.5994465014
standard deviation is 3.7842768957
standard deviation is 3.76760705638
standard deviation is 3.93105038429
standard deviation is 2.94690317458
standard deviation is 3.46660737528
standard deviation is 3.86932682109
standard deviation is 3.19571440861
standard deviation is 3.21471392671
standard deviation is 3.41499956132
standard deviation is 2.73417698283
standard deviation is 3.8423087534
standard deviation is 3.74496777538
standard deviation is 3.61773649728
standard deviation is 3.99828831384
standard deviation is 2.97462522947
standard deviation is 3.51180720468
standard deviation is 3.53286277084
standard deviation is 3.8769922273
standard deviation is 3.01843345583
standard deviation is 3.55200832146
C:\Program Files\Anaconda3\lib\site-packages\numpy\lib\nanfunctions.py:703: RuntimeWarning: Mean of empty slice
warnings.warn("Mean of empty slice", RuntimeWarning)
standard deviation is nan
C:\Program Files\Anaconda3\lib\site-packages\numpy\lib\nanfunctions.py:703: RuntimeWarning: Mean of empty slice
warnings.warn("Mean of empty slice", RuntimeWarning)
standard deviation is nan
standard deviation is 3.59222072684
standard deviation is 3.21508449045
standard deviation is 2.43887891229
standard deviation is 2.84117539626
standard deviation is 4.15791921042
standard deviation is 1.94461638021
[3.4993612495580368, 3.6870306571611344, 3.7488058768225527, 3.0375338296172174, 3.5994465013966233, 3.784276895700394, 3.7676070563774249, 3.9310503842926576, 2.9469031745838019, 3.4666073752802822, 3.8693268210946399, 3.1957144086139548, 3.2147139267096514, 3.4149995613224315, 2.7341769828343323, 3.8423087534031906, 3.744967775378214, 3.6177364972793216, 3.9982883138418761, 2.9746252294687774, 3.5118072046767335, 3.532862770841172, 3.8769922273043442, 3.0184334558342241, 3.5520083214647347, nan, nan, 3.5922207268351505, 3.2150844904493332, 2.438878912286393, 2.8411753962604567, 4.1579192104221345, 1.944616380212306] [66, 54, 61, 92, 88, 75, 89, 103, 66, 75, 57, 46, 48, 53, 26, 64, 13, 52, 38, 81, 42, 37, 28, 47, 57, 0, 0, 15, 24, 49, 51, 20, 14] [3.4993612495580368, 3.6870306571611344, 3.7488058768225527, 3.0375338296172174, 3.5994465013966233, 3.784276895700394, 3.7676070563774249, 3.9310503842926576, 2.9469031745838019, 3.4666073752802822, 3.8693268210946399, 3.1957144086139548, 3.2147139267096514, 3.4149995613224315, 2.7341769828343323, 3.8423087534031906, 3.744967775378214, 3.6177364972793216, 3.9982883138418761, 2.9746252294687774, 3.5118072046767335, 3.532862770841172, 3.8769922273043442, 3.0184334558342241, 3.5520083214647347, nan, nan, 3.5922207268351505, 3.2150844904493332, 2.438878912286393, 2.8411753962604567, 4.1579192104221345, 1.944616380212306] [66, 54, 61, 92, 88, 75, 89, 103, 66, 75, 57, 46, 48, 53, 26, 64, 13, 52, 38, 81, 42, 37, 28, 47, 57, 0, 0, 15, 24, 49, 51, 20, 14] ['116_1038408484', '115_1038307052', '116_1038307052', '115_1038305585', '116_1038305585', '115_1038408318', '116_1038408318', '115_1038408484', '115_1038305594', '115_1038407804', '116_1038305594', '116_1038407804', '115_1038407806', '116_1038407806', '115_1038408272', '115_1038408104', '116_1038408272', '116_1038408104', '115_1038407334', '115_F005V16903', '116_1038407334', '115_1038408462', '116_1038408462', '115_1038407754', '116_1038407754', '115_1038305588', '116_1038305588', '115_1038407332', '116_1038407332', '115_1038408840', '116_1038408840', '115_1038305359', '116_1038305359'] [3.4993612495580368, 3.6870306571611344, 3.7488058768225527, 3.0375338296172174, 3.5994465013966233, 3.784276895700394, 3.7676070563774249, 3.9310503842926576, 2.9469031745838019, 3.4666073752802822, 3.8693268210946399, 3.1957144086139548, 3.2147139267096514, 3.4149995613224315, 2.7341769828343323, 3.8423087534031906, 3.744967775378214, 3.6177364972793216, 3.9982883138418761, 2.9746252294687774, 3.5118072046767335, 3.532862770841172, 3.8769922273043442, 3.0184334558342241, 3.5520083214647347, 3.5922207268351505, 3.2150844904493332, 2.438878912286393, 2.8411753962604567, 4.1579192104221345, 1.944616380212306] [66, 54, 61, 92, 88, 75, 89, 103, 66, 75, 57, 46, 48, 53, 26, 64, 13, 52, 38, 81, 42, 37, 28, 47, 57, 15, 24, 49, 51, 20, 14] ['116_1038408484', '115_1038307052', '116_1038307052', '115_1038305585', '116_1038305585', '115_1038408318', '116_1038408318', '115_1038408484', '115_1038305594', '115_1038407804', '116_1038305594', '116_1038407804', '115_1038407806', '116_1038407806', '115_1038408272', '115_1038408104', '116_1038408272', '116_1038408104', '115_1038407334', '115_F005V16903', '116_1038407334', '115_1038408462', '116_1038408462', '115_1038407754', '116_1038407754', '115_1038407332', '116_1038407332', '115_1038408840', '116_1038408840', '115_1038305359', '116_1038305359'] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 27, 28, 29, 30, 31, 32]
C:\Program Files\Anaconda3\lib\site-packages\matplotlib\axes\_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.
warnings.warn("No labelled objects found. "
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